ISL Defender Comparative Analysis

Import all required libraries

# Data Manipulation libraries:
import io
import pandas as pd
from copy import deepcopy
from sklearn.preprocessing import MinMaxScaler

# Plotting libraries:
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Import required data

This code is to import and read a csv file stored in your local system

isl_df = pd.read_csv(r"C:\Users\AMIT PAREEK\Documents\project deploment\Football-Anaytics-with-Python-main\isl_player_final.csv")
isl_df.head()

	tour_id	tour_name	id	jersey_no	name	short_name	position_id	position	position_short	team_id	...	goaltenders.catches	goaltenders.punches	goaltenders.penalty_kicks.shots_faced	goaltenders.penalty_kicks.goals_allowed	goaltenders.penalty_kicks.saves	country_id	country_name	dob	player_foot	height
0	148	ISL6	1514	3	Asamoah Gyan	Asamoah Gyan	2	Forward	FWD	504	...	0	0	0	0	0	43	Ghana	NaN	NaN	NaN
1	148	ISL6	2475	26	Deshorn Dwayne Brown	Deshorn Brown	2	Forward	FWD	656	...	0	0	0	0	0	56	Jamaica	12/22/1990	Right	187.0
2	148	ISL6	2900	9	Amine Chermiti	Amine Chermiti	2	Forward	FWD	506	...	0	0	0	0	0	106	Tunisia	12/26/1987	left	177.0
3	148	ISL6	3753	7	Francisco Medina Luna	Piti	3	Midfielder	MF	1159	...	0	0	0	0	0	19	Spain	5/26/1981	left	179.0
4	148	ISL6	3809	4	Rafael Lopez Gomez	Rafael Lopez Gomez	1	Defender	DEF	1536	...	0	0	0	0	0	19	Spain	4/9/1985	right	183.0

5 rows × 93 columns

Data Preparation

Explore and prepare your data for the required analysis to be performed. Eg., we are going to analyse goalkeepers, so we need to filter out and remove all the data from positions other than GK.

# Check all the unique positions in the data:
isl_df.drop_duplicates(subset=["position_id"])[["position_id", "position"]]

	position_id	position
0	2	Forward
3	3	Midfielder
4	1	Defender
9	4	Goalkeeper

Slicing and Storing for only Defender data

Pro-tip: It is an expert practice to always use deepcopy to save sliced dataframes in a new variable

# Slice (Filter) dataframe only to retain information about GKs:
isl_df[(isl_df["minutes_played"] > 1500) & (isl_df["country_id"] == 1)].shape
isl_df[(isl_df["position_id"] == 1) & (isl_df["minutes_played"] > 1000) & (isl_df["country_id"] == 1)]

	tour_id	tour_name	id	jersey_no	name	short_name	position_id	position	position_short	team_id	...	goaltenders.catches	goaltenders.punches	goaltenders.penalty_kicks.shots_faced	goaltenders.penalty_kicks.goals_allowed	goaltenders.penalty_kicks.saves	country_id	country_name	dob	player_foot	height
26	148	ISL6	10256	21	Narayan Das	Narayan Das	1	Defender	DEF	1499	...	0	0	0	0	0	1	India	9/25/1993	Left	175.0
28	148	ISL6	10624	33	Prabir Das	Prabir Das	1	Defender	DEF	499	...	0	0	0	0	0	1	India	12/20/1993	Right	172.0
44	148	ISL6	10694	10	Harmanjot Singh Khabra	Harmanjot Khabra	1	Defender	DEF	656	...	0	0	0	0	0	1	India	3/18/1988	right	181.0
47	148	ISL6	10712	20	Pritam Kotal	Pritam Kotal	1	Defender	DEF	499	...	0	0	0	0	0	1	India	8/9/1993	Right	179.0
50	148	ISL6	10770	5	Raju Eknath Gaikwad	Raju Gaikwad	1	Defender	DEF	498	...	0	0	0	0	0	1	India	9/25/1990	Right	180.0
67	148	ISL6	16577	13	Gurtej Singh	Gurtej Singh	1	Defender	DEF	1536	...	0	0	0	0	0	1	India	5/2/1990	right	181.0
76	148	ISL6	19129	12	Keisham Reagan Singh	Reagan Singh	1	Defender	DEF	504	...	0	0	0	0	0	1	India	4/1/1991	Right	173.0
91	148	ISL6	21201	2	Rahul Shankar Bheke	Rahul Bheke	1	Defender	DEF	656	...	0	0	0	0	0	1	India	12/6/1990	right	179.0
107	148	ISL6	26616	2	Pratik Prabhakar Chaudhari	Pratik Chaudhari	1	Defender	DEF	506	...	0	0	0	0	0	1	India	10/4/1989	right	186.0
111	148	ISL6	26713	18	Jerry Lalrinzuala	Jerry Lalrinzuala	1	Defender	DEF	505	...	0	0	0	0	0	1	India	7/13/1998	Left	0.0
125	148	ISL6	30980	22	Nishu Kumar Tashni	Nishu Kumar	1	Defender	DEF	656	...	0	0	0	0	0	1	India	1/1/1997	Left	178.0
134	148	ISL6	31824	20	Seriton Benny Fernandes	Seriton Fernandes	1	Defender	DEF	496	...	0	0	0	0	0	1	India	6/1/1993	Right	0.0
146	148	ISL6	31875	15	Subhasish Prodyut Bose	Subhasish Bose	1	Defender	DEF	506	...	0	0	0	0	0	1	India	8/18/1995	Left	186.0
168	148	ISL6	35065	16	Sarthak Golui	Sarthak Golui	1	Defender	DEF	506	...	0	0	0	0	0	1	India	3/11/1997	Right	187.0
181	148	ISL6	35281	33	Rakesh Pradhan	Rakesh Pradhan	1	Defender	DEF	504	...	0	0	0	0	0	1	India	8/2/1997	NaN	NaN
193	148	ISL6	36329	44	Asish Rai	Asish Rai	1	Defender	DEF	1536	...	0	0	0	0	0	1	India	2/2/1999	Right	176.0
195	148	ISL6	36441	2	Sumit Rathi	Sumit Rathi	1	Defender	DEF	499	...	0	0	0	0	0	1	India	26-08-2001	NaN	NaN
200	148	ISL6	36795	15	Shubham Sarangi	Shubham Sarangi	1	Defender	DEF	1499	...	0	0	0	0	0	1	India	6/24/2000	right	179.0
216	148	ISL6	55646	2	Mohamad Rakip	Mohamad Rakip	1	Defender	DEF	498	...	0	0	0	0	0	1	India	5/14/2000	right	0.0
244	148	ISL6	68806	14	Jessel Allan Carneiro	Jessel Carneiro	1	Defender	DEF	498	...	0	0	0	0	0	1	India	NaN	NaN	NaN
265	202	ISL7	3988	5	Gurjinder Kumar	Gurjinder Kumar	1	Defender	DEF	504	...	0	0	0	0	0	1	India	NaN	NaN	NaN
287	202	ISL7	10255	17	Mandar Rao Dessai	Mandar Rao Dessai	1	Defender	DEF	506	...	0	0	0	0	0	1	India	3/18/1992	Left	171.0
288	202	ISL7	10256	21	Narayan Das	Narayan Das	1	Defender	DEF	1102	...	0	0	0	0	0	1	India	9/25/1993	Left	175.0
289	202	ISL7	10624	33	Prabir Das	Prabir Das	1	Defender	DEF	1874	...	0	0	0	0	0	1	India	12/20/1993	Right	172.0
291	202	ISL7	10641	5	Sandesh Jhingan	Sandesh Jhingan	1	Defender	DEF	1874	...	0	0	0	0	0	1	India	NaN	NaN	NaN
304	202	ISL7	10710	12	Ashutosh Mehta	Ashutosh Mehta	1	Defender	DEF	504	...	0	0	0	0	0	1	India	NaN	NaN	NaN
305	202	ISL7	10712	20	Pritam Kotal	Pritam Kotal	1	Defender	DEF	1874	...	0	0	0	0	0	1	India	8/9/1993	Right	179.0
326	202	ISL7	19129	2	Keisham Reagan Singh	Reagan Singh	1	Defender	DEF	505	...	0	0	0	0	0	1	India	4/1/1991	Right	173.0
342	202	ISL7	21201	2	Rahul Shankar Bheke	Rahul Bheke	1	Defender	DEF	656	...	0	0	0	0	0	1	India	12/6/1990	right	179.0
358	202	ISL7	26611	4	Chinglensana Singh Konsham	Chinglensana Singh	1	Defender	DEF	1536	...	0	0	0	0	0	1	India	11/23/1996	Right	186.0
362	202	ISL7	26713	18	Jerry Lalrinzuala	Jerry Lalrinzuala	1	Defender	DEF	505	...	0	0	0	0	0	1	India	7/13/1998	Left	0.0
380	202	ISL7	31791	4	Amey Ganesh Ranawade	Amey Ranawade	1	Defender	DEF	506	...	0	0	0	0	0	1	India	3/7/1998	NaN	0.0
386	202	ISL7	31824	20	Seriton Benny Fernandes	Seriton Fernandes	1	Defender	DEF	496	...	0	0	0	0	0	1	India	6/1/1993	Right	0.0
396	202	ISL7	31875	15	Subhasish Prodyut Bose	Subhasish Bose	1	Defender	DEF	1874	...	0	0	0	0	0	1	India	8/18/1995	Left	186.0
428	202	ISL7	35224	26	Laldinliana Renthlei	Laldinliana Renthlei	1	Defender	DEF	1159	...	0	0	0	0	0	1	India	8/26/1998	NaN	0.0
435	202	ISL7	35274	16	Ricky Lallawmawma	Ricky Lallawmawma	1	Defender	DEF	1159	...	0	0	0	0	0	1	India	9/9/1991	Left	0.0
447	202	ISL7	36315	22	Deepak Tangri	Deepak Tangri	1	Defender	DEF	505	...	0	0	0	0	0	1	India	1/1/1999	Right	177.0
448	202	ISL7	36317	13	Gaurav Bora	Gaurav Bora	1	Defender	DEF	1499	...	0	0	0	0	0	1	India	7/13/1998	right	175.0
455	202	ISL7	36329	44	Asish Rai	Asish Rai	1	Defender	DEF	1536	...	0	0	0	0	0	1	India	2/2/1999	Right	176.0
485	202	ISL7	55653	21	Saviour Gama	Saviour Gama	1	Defender	DEF	496	...	0	0	0	0	0	1	India	0000-00-00	NaN	0.0
493	202	ISL7	58793	3	Soraisham Sandeep Singh	Sandeep Singh	1	Defender	DEF	498	...	0	0	0	0	0	1	India	NaN	NaN	NaN
499	202	ISL7	63446	31	Akash Mishra	Akash Mishra	1	Defender	DEF	1536	...	0	0	0	0	0	1	India	NaN	NaN	NaN
517	202	ISL7	68806	14	Jessel Allan Carneiro	Jessel Carneiro	1	Defender	DEF	498	...	0	0	0	0	0	1	India	NaN	NaN	NaN

43 rows × 93 columns

# Save the sliced dataframe in a variable for further use:
indian_defenders= deepcopy(isl_df[(isl_df["position_id"] == 1) & (isl_df["minutes_played"] > 1000) & (isl_df["country_id"] == 1)])

indian_defenders.reset_index(drop=True,inplace=True)

Exploration of defender data

Meta-Level information

# First thing to always do before starting analysis is to see generic meta-level information about your dataframe:
indian_defenders.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 43 entries, 0 to 42
Data columns (total 93 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   tour_id                                  43 non-null     int64  
 1   tour_name                                43 non-null     object 
 2   id                                       43 non-null     int64  
 3   jersey_no                                43 non-null     int64  
 4   name                                     43 non-null     object 
 5   short_name                               43 non-null     object 
 6   position_id                              43 non-null     int64  
 7   position                                 43 non-null     object 
 8   position_short                           43 non-null     object 
 9   team_id                                  43 non-null     int64  
 10  team_name                                43 non-null     object 
 11  team_short_name                          43 non-null     object 
 12  is_started                               43 non-null     int64  
 13  is_onbench                               43 non-null     int64  
 14  is_goalkeeper                            43 non-null     int64  
 15  is_substitute                            43 non-null     int64  
 16  minutes_played                           43 non-null     int64  
 17  bonus_points                             43 non-null     int64  
 18  injury_minutes_played                    43 non-null     int64  
 19  actual_minutes_played                    43 non-null     int64  
 20  goaltenders                              43 non-null     int64  
 21  events.goals                             43 non-null     int64  
 22  events.own_goals                         43 non-null     int64  
 23  events.assists                           43 non-null     int64  
 24  events.key_passes                        43 non-null     int64  
 25  events.chances_created                   43 non-null     int64  
 26  events.shots                             43 non-null     int64  
 27  events.shots_on_target                   43 non-null     int64  
 28  events.shots_off_target                  43 non-null     int64  
 29  events.fouls_committed                   43 non-null     int64  
 30  events.fouls_suffered                    43 non-null     int64  
 31  events.yellow_cards                      43 non-null     int64  
 32  events.red_cards                         43 non-null     int64  
 33  events.is_second_yellow_card             43 non-null     int64  
 34  events.offsides                          43 non-null     int64  
 35  events.corner_kicks                      43 non-null     int64  
 36  events.crosses                           43 non-null     int64  
 37  events.free_kicks                        43 non-null     int64  
 38  events.throw_in                          43 non-null     int64  
 39  events.punches                           43 non-null     int64  
 40  events.catches                           43 non-null     int64  
 41  events.goals_conceded                    43 non-null     int64  
 42  events.clean_sheet                       43 non-null     int64  
 43  events.cleansheet                        43 non-null     int64  
 44  events.fantasy_assists                   43 non-null     int64  
 45  events.penalties_saved                   43 non-null     int64  
 46  events.penalties_earned                  43 non-null     int64  
 47  events.penalties_conceded                43 non-null     int64  
 48  events.penalties_missed                  43 non-null     int64  
 49  events.freekicks_conceded_goal           43 non-null     int64  
 50  events.freekicks_earned_goal             43 non-null     int64  
 51  events.blocked_shots                     43 non-null     int64  
 52  events.defensive_blocked_shots           43 non-null     int64  
 53  events.penalty_kicks.total               43 non-null     int64  
 54  events.penalty_kicks.goals               43 non-null     int64  
 55  touches.total                            43 non-null     int64  
 56  touches.total_passes                     43 non-null     int64  
 57  touches.good_passes                      43 non-null     int64  
 58  touches.bad_passes                       43 non-null     int64  
 59  touches.interceptions                    43 non-null     int64  
 60  touches.blocks                           43 non-null     int64  
 61  touches.tackles                          43 non-null     int64  
 62  touches.successful_tackles               43 non-null     int64  
 63  touches.unsuccessful_tackles             43 non-null     int64  
 64  touches.clearance                        43 non-null     int64  
 65  touches.saves                            43 non-null     int64  
 66  touches.take_on_total                    43 non-null     int64  
 67  touches.take_on_successful               43 non-null     int64  
 68  touches.take_on_unsuccessful             43 non-null     int64  
 69  touches.last_man_tackle_successful       43 non-null     int64  
 70  touches.last_man_tackle_unsuccessful     43 non-null     int64  
 71  touches.successful_passes                43 non-null     int64  
 72  touches.interceptions_won                43 non-null     int64  
 73  touches.aerial_duel.total                43 non-null     int64  
 74  touches.aerial_duel.won                  43 non-null     int64  
 75  touches.aerial_duel.lost                 43 non-null     int64  
 76  touches.ground_duel.total                43 non-null     int64  
 77  touches.ground_duel.won                  43 non-null     int64  
 78  touches.ground_duel.lost                 43 non-null     int64  
 79  goaltenders.shots_faced                  43 non-null     int64  
 80  goaltenders.shots_on_goal_faced          43 non-null     int64  
 81  goaltenders.goals_allowed                43 non-null     int64  
 82  goaltenders.saves                        43 non-null     int64  
 83  goaltenders.catches                      43 non-null     int64  
 84  goaltenders.punches                      43 non-null     int64  
 85  goaltenders.penalty_kicks.shots_faced    43 non-null     int64  
 86  goaltenders.penalty_kicks.goals_allowed  43 non-null     int64  
 87  goaltenders.penalty_kicks.saves          43 non-null     int64  
 88  country_id                               43 non-null     int64  
 89  country_name                             43 non-null     object 
 90  dob                                      36 non-null     object 
 91  player_foot                              31 non-null     object 
 92  height                                   34 non-null     float64
dtypes: float64(1), int64(82), object(10)
memory usage: 31.4+ KB

Basic Statistical exploration

# After you have fairly had an overview of your dataframe, next step is to look at some basic statistics for numerical columns:
indian_defenders.describe().round(1)

	tour_id	id	jersey_no	position_id	team_id	is_started	is_onbench	is_goalkeeper	is_substitute	minutes_played	...	goaltenders.shots_on_goal_faced	goaltenders.goals_allowed	goaltenders.saves	goaltenders.catches	goaltenders.punches	goaltenders.penalty_kicks.shots_faced	goaltenders.penalty_kicks.goals_allowed	goaltenders.penalty_kicks.saves	country_id	height
count	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	...	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	34.0
mean	176.9	28949.3	16.0	1.0	878.1	16.2	3.2	0.0	3.4	1443.7	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	131.2
std	27.3	16937.0	11.0	0.0	513.9	3.2	2.4	0.0	3.2	270.7	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	80.0
min	148.0	3988.0	2.0	1.0	496.0	11.0	0.0	0.0	0.0	1002.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
25%	148.0	10741.0	5.0	1.0	501.5	13.0	1.0	0.0	1.5	1183.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	42.8
50%	202.0	30980.0	15.0	1.0	506.0	17.0	3.0	0.0	3.0	1504.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	175.5
75%	202.0	36316.0	21.0	1.0	1499.0	18.0	5.0	0.0	4.5	1620.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	179.0
max	202.0	68806.0	44.0	1.0	1874.0	22.0	7.0	0.0	13.0	1980.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	187.0

8 rows × 83 columns

Display all columns

Pro-tip: If all the columns of a datarame are not displayed, use the pandas set_option function to display any number of columns you wish.

# We will set display max columns to 85 since we have 83 columns in our gk_df:
pd.set_option("display.max_columns", 85)

# Let's run the describe function again to see the change:
indian_defenders.describe().round(1)

	tour_id	id	jersey_no	position_id	team_id	is_started	is_onbench	is_goalkeeper	is_substitute	minutes_played	bonus_points	injury_minutes_played	actual_minutes_played	goaltenders	events.goals	events.own_goals	events.assists	events.key_passes	events.chances_created	events.shots	events.shots_on_target	events.shots_off_target	events.fouls_committed	events.fouls_suffered	events.yellow_cards	events.red_cards	events.is_second_yellow_card	events.offsides	events.corner_kicks	events.crosses	events.free_kicks	events.throw_in	events.punches	events.catches	events.goals_conceded	events.clean_sheet	events.cleansheet	events.fantasy_assists	events.penalties_saved	events.penalties_earned	events.penalties_conceded	events.penalties_missed	events.freekicks_conceded_goal	events.freekicks_earned_goal	events.blocked_shots	events.defensive_blocked_shots	events.penalty_kicks.total	events.penalty_kicks.goals	touches.total	touches.total_passes	touches.good_passes	touches.bad_passes	touches.interceptions	touches.blocks	touches.tackles	touches.successful_tackles	touches.unsuccessful_tackles	touches.clearance	touches.saves	touches.take_on_total	touches.take_on_successful	touches.take_on_unsuccessful	touches.last_man_tackle_successful	touches.last_man_tackle_unsuccessful	touches.successful_passes	touches.interceptions_won	touches.aerial_duel.total	touches.aerial_duel.won	touches.aerial_duel.lost	touches.ground_duel.total	touches.ground_duel.won	touches.ground_duel.lost	goaltenders.shots_faced	goaltenders.shots_on_goal_faced	goaltenders.goals_allowed	goaltenders.saves	goaltenders.catches	goaltenders.punches	goaltenders.penalty_kicks.shots_faced	goaltenders.penalty_kicks.goals_allowed	goaltenders.penalty_kicks.saves	country_id	height
count	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	43.0	34.0
mean	176.9	28949.3	16.0	1.0	878.1	16.2	3.2	0.0	3.4	1443.7	0.0	84.3	1534.2	0.0	0.2	0.0	0.8	7.9	8.6	5.8	1.8	2.8	18.3	15.2	3.3	0.1	0.1	0.8	2.1	27.2	8.2	127.1	0.0	0.0	18.3	0.0	5.2	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.2	0.0	0.0	0.0	840.2	549.2	372.5	176.7	27.1	25.9	52.0	18.6	33.4	67.0	0.0	31.6	6.6	2.8	0.1	0.0	0.0	0.0	19.6	10.3	9.3	0.3	0.2	0.1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	131.2
std	27.3	16937.0	11.0	0.0	513.9	3.2	2.4	0.0	3.2	270.7	0.0	24.0	288.3	0.0	0.4	0.2	1.2	4.2	5.0	4.0	1.8	2.0	7.9	7.6	1.7	0.4	0.3	1.0	7.2	16.9	7.1	64.2	0.0	0.3	5.8	0.0	3.3	1.3	0.0	0.0	0.0	0.0	0.0	0.0	1.6	0.0	0.0	0.0	189.1	133.8	105.7	57.3	9.7	9.6	18.8	7.6	12.7	32.5	0.0	26.7	5.7	2.9	0.3	0.0	0.0	0.0	11.1	7.1	5.2	0.5	0.5	0.3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	80.0
min	148.0	3988.0	2.0	1.0	496.0	11.0	0.0	0.0	0.0	1002.0	0.0	45.0	1063.0	0.0	0.0	0.0	0.0	1.0	2.0	0.0	0.0	0.0	5.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	500.0	295.0	158.0	64.0	11.0	10.0	17.0	5.0	12.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	1.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
25%	148.0	10741.0	5.0	1.0	501.5	13.0	1.0	0.0	1.5	1183.0	0.0	64.0	1261.0	0.0	0.0	0.0	0.0	5.0	5.5	2.0	1.0	1.0	13.0	9.0	2.0	0.0	0.0	0.0	0.0	15.0	3.5	86.0	0.0	0.0	14.5	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	659.5	434.5	293.5	125.0	20.5	18.0	39.0	13.0	25.0	45.5	0.0	7.0	3.5	0.0	0.0	0.0	0.0	0.0	11.0	5.0	5.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	42.8
50%	202.0	30980.0	15.0	1.0	506.0	17.0	3.0	0.0	3.0	1504.0	0.0	83.0	1593.0	0.0	0.0	0.0	1.0	7.0	7.0	5.0	1.0	2.0	18.0	15.0	4.0	0.0	0.0	1.0	0.0	28.0	7.0	129.0	0.0	0.0	18.0	0.0	6.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	837.0	547.0	374.0	179.0	26.0	25.0	48.0	18.0	32.0	59.0	0.0	28.0	5.0	2.0	0.0	0.0	0.0	0.0	17.0	10.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	175.5
75%	202.0	36316.0	21.0	1.0	1499.0	18.0	5.0	0.0	4.5	1620.0	0.0	103.0	1727.5	0.0	0.0	0.0	1.0	10.0	10.5	8.5	2.5	4.0	21.0	19.5	4.0	0.0	0.0	1.0	0.0	39.0	11.0	172.0	0.0	0.0	22.0	0.0	7.5	1.5	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	994.5	655.0	442.0	221.5	32.5	32.0	65.0	24.0	42.0	83.0	0.0	58.5	8.5	4.0	0.0	0.0	0.0	0.0	26.0	13.5	12.5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	179.0
max	202.0	68806.0	44.0	1.0	1874.0	22.0	7.0	0.0	13.0	1980.0	0.0	139.0	2119.0	0.0	1.0	1.0	5.0	20.0	26.0	17.0	8.0	8.0	45.0	33.0	7.0	1.0	1.0	4.0	38.0	66.0	34.0	228.0	0.0	2.0	36.0	0.0	12.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	0.0	0.0	0.0	1250.0	827.0	625.0	319.0	55.0	51.0	101.0	38.0	63.0	149.0	0.0	78.0	24.0	10.0	1.0	0.0	0.0	0.0	55.0	30.0	25.0	2.0	2.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	187.0

Level the playing Field

# Numerical exploration:
defender_mins_df = indian_defenders.groupby(["id", "name"])["minutes_played"].sum().reset_index()

defender_mins_df

	id	name	minutes_played
0	3988	Gurjinder Kumar	1519
1	10255	Mandar Rao Dessai	1082
2	10256	Narayan Das	2982
3	10624	Prabir Das	2860
4	10641	Sandesh Jhingan	1892
5	10694	Harmanjot Singh Khabra	1619
6	10710	Ashutosh Mehta	1406
7	10712	Pritam Kotal	3870
8	10770	Raju Eknath Gaikwad	1022
9	16577	Gurtej Singh	1035
10	19129	Keisham Reagan Singh	3135
11	21201	Rahul Shankar Bheke	2345
12	26611	Chinglensana Singh Konsham	1523
13	26616	Pratik Prabhakar Chaudhari	1488
14	26713	Jerry Lalrinzuala	2649
15	30980	Nishu Kumar Tashni	1466
16	31791	Amey Ganesh Ranawade	1564
17	31824	Seriton Benny Fernandes	3338
18	31875	Subhasish Prodyut Bose	3181
19	35065	Sarthak Golui	1148
20	35224	Laldinliana Renthlei	1237
21	35274	Ricky Lallawmawma	1674
22	35281	Rakesh Pradhan	1350
23	36315	Deepak Tangri	1007
24	36317	Gaurav Bora	1620
25	36329	Asish Rai	2554
26	36441	Sumit Rathi	1170
27	36795	Shubham Sarangi	1409
28	55646	Mohamad Rakip	1290
29	55653	Saviour Gama	1878
30	58793	Soraisham Sandeep Singh	1150
31	63446	Akash Mishra	1800
32	68806	Jessel Allan Carneiro	2816

px.bar(x="name", y="minutes_played", text="minutes_played", data_frame=defender_mins_df)

indian_defenders["interceptionsPer90"] = indian_defenders["touches.interceptions"].divide(indian_defenders["minutes_played"]).multiply(90)

indian_defenders["blocksPer90"] = indian_defenders["touches.blocks"].divide(indian_defenders["minutes_played"]).multiply(90)
indian_defenders["tacklesPer90"] = indian_defenders["touches.tackles"].divide(indian_defenders["minutes_played"]).multiply(90)
indian_defenders["clearancePer90"] = indian_defenders["touches.clearance"].divide(indian_defenders["minutes_played"]).multiply(90)
indian_defenders["tackles_sucessPer90"] = indian_defenders["touches.successful_tackles"].divide(indian_defenders["minutes_played"]).multiply(90)

px.bar(x="name", y="blocksPer90", text="blocksPer90", data_frame=indian_defenders)

px.bar(x="name", y="tacklesPer90", text="tacklesPer90", data_frame=indian_defenders)

px.bar(x="name", y="interceptionsPer90", text="interceptionsPer90", data_frame=indian_defenders)

px.bar(x="name", y="clearancePer90", text="clearancePer90", data_frame=indian_defenders)

px.bar(x="name", y="tackles_sucessPer90", text="tackles_sucessPer90", data_frame=indian_defenders)

cols_for_radar=["interceptionsPer90","blocksPer90","tacklesPer90","clearancePer90","tackles_sucessPer90"]

Normalization of Values

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
indian_defenders[cols_for_radar] = scaler.fit_transform(indian_defenders[cols_for_radar])

for i, row in indian_defenders.iterrows():
    print(row["name"])
    fig = px.line_polar(indian_defenders, r=indian_defenders.loc[(indian_defenders["id"] == row["id"]), cols_for_radar].sum(),
                        theta=cols_for_radar, line_close=True,
                        title=row["name"])
    fig.update_traces(fill='toself')
    fig.show()

Narayan Das

Prabir Das

Harmanjot Singh Khabra

Pritam Kotal

Raju Eknath Gaikwad

Gurtej Singh

Keisham Reagan Singh

Rahul Shankar Bheke

Pratik Prabhakar Chaudhari

Jerry Lalrinzuala

Nishu Kumar Tashni

Seriton Benny Fernandes

Subhasish Prodyut Bose

Sarthak Golui

Rakesh Pradhan

Asish Rai

Sumit Rathi

Shubham Sarangi

Mohamad Rakip

Jessel Allan Carneiro

Gurjinder Kumar

Mandar Rao Dessai

Narayan Das

Prabir Das

Sandesh Jhingan

Ashutosh Mehta

Pritam Kotal

Keisham Reagan Singh

Rahul Shankar Bheke

Chinglensana Singh Konsham

Jerry Lalrinzuala

Amey Ganesh Ranawade

Seriton Benny Fernandes

Subhasish Prodyut Bose

Laldinliana Renthlei

Ricky Lallawmawma

Deepak Tangri

Gaurav Bora

Asish Rai

Saviour Gama

Soraisham Sandeep Singh

Akash Mishra

Jessel Allan Carneiro

Summary

We have analyzed various aspect for finding best defender among all the parameters that we have sets. important feature selection had done on the data and tackles,interception,clearnance,blocks are very import parameter to decide a best defenders.After this I can say that Ashish rai is front runner in terms of statics and followed by Subhasish Prodyut Bose,Pritam Kotal.